Developing and Deploying Applications with Streamlit

About Developing and Deploying Applications with Streamlit

Streamlit is an open-source app framework for Machine Learning and Data Science teams.

Streamlit lets you turn data scripts into shareable web apps in minutes. It’s all Python, open-source, and free! And once you’ve created an app you can use our cloud platform to deploy, manage, and share your app!

In this course we will cover everything you need to know concerning streamlit such as

Installing Anaconda and create a virtual env

Installing Streamlit , pytube, firebase

Setting up GitHub account if you already don't have one

Display Information with Streamlit

Widgets with Streamlit 

Working with data frames ( Loading , Displaying )

Creating a image filter ( we use popular Instagram filters)

Creating a YouTube video downloader (using pytube api)

pytube is a lightweight, dependency-free Python library which is used for downloading videos from the web

Creating Interactive plots

User selected input value for chart

Animated Plot

Introduction to Multipage Apps

Structuring multipage apps

Run a multipage app

Adding pages

Adding Authentication to your  Streamlit app using Streamlit-Authenticator

Authentication via Pickle File

Authentication via  Database

Build a Word Cloud App

Build a OCR - Image to text conversion with tesseract

Build a World Cloud App

ChatGPT + Streamlit

Build a auto review response generator with chatGPT and Open AI 

Build a Leetcode problem solver with chatGPT and Open AI 

Content in progress to be uploaded soon

Creating  a personal portfolio page with streamlit

Deploy Application with Streamlit  Cloud

Concept of Sessions

NTLK with streamlit

Working with SQLite

Connecting to database

Reading data from database

Writing Data  into database

Additional Apps

Static Code quality analyzer

No SQL Job Board with Firebase  API

Converting random forest model into streamlit application